Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus

Jayashree S. Shetty; Nisha P. Shetty; Vedant Rishi Das; Vaibhav; Diana Olivia

doi:10.1007/978-981-19-4193-1_20

Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus

Jayashree S. Shetty
, Nisha P. Shetty^*
, Vedant Rishi Das
, Vaibhav
, Diana Olivia

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80% was recorded on the ensemble method.

Original language	English
Title of host publication	Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022
Editors	Paramartha Dutta, Satyajit Chakrabarti, Abhishek Bhattacharya, Soumi Dutta, Vincenzo Piuri
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	215-223
Number of pages	9
ISBN (Print)	9789811941924
DOIs	https://doi.org/10.1007/978-981-19-4193-1_20
Publication status	Published - 2023
Event	3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022 - Kolkata, India Duration: 23-02-2022 → 25-02-2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	491
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022
Country/Territory	India
City	Kolkata
Period	23-02-22 → 25-02-22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-19-4193-1_20

Cite this

Shetty, J. S., Shetty, N. P., Das, V. R., Vaibhav, & Olivia, D. (2023). Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus. In P. Dutta, S. Chakrabarti, A. Bhattacharya, S. Dutta, & V. Piuri (Eds.), Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022 (pp. 215-223). (Lecture Notes in Networks and Systems; Vol. 491). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-4193-1_20

Shetty, Jayashree S. ; Shetty, Nisha P. ; Das, Vedant Rishi et al. / Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus. Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022. editor / Paramartha Dutta ; Satyajit Chakrabarti ; Abhishek Bhattacharya ; Soumi Dutta ; Vincenzo Piuri. Springer Science and Business Media Deutschland GmbH, 2023. pp. 215-223 (Lecture Notes in Networks and Systems).

@inproceedings{00ee240fc8eb419dbb011f3818e7b43d,

title = "Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus",

abstract = "Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80\% was recorded on the ensemble method.",

author = "Shetty, \{Jayashree S.\} and Shetty, \{Nisha P.\} and Das, \{Vedant Rishi\} and Vaibhav and Diana Olivia",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022 ; Conference date: 23-02-2022 Through 25-02-2022",

year = "2023",

doi = "10.1007/978-981-19-4193-1\_20",

language = "English",

isbn = "9789811941924",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "215--223",

editor = "Paramartha Dutta and Satyajit Chakrabarti and Abhishek Bhattacharya and Soumi Dutta and Vincenzo Piuri",

booktitle = "Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022",

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Shetty, JS , Shetty, NP, Das, VR, Vaibhav, & Olivia, D 2023, Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus. in P Dutta, S Chakrabarti, A Bhattacharya, S Dutta & V Piuri (eds), Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022. Lecture Notes in Networks and Systems, vol. 491, Springer Science and Business Media Deutschland GmbH, pp. 215-223, 3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022, Kolkata, India, 23-02-22. https://doi.org/10.1007/978-981-19-4193-1_20

Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus. / Shetty, Jayashree S.; Shetty, Nisha P.; Das, Vedant Rishi et al.
Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022. ed. / Paramartha Dutta; Satyajit Chakrabarti; Abhishek Bhattacharya; Soumi Dutta; Vincenzo Piuri. Springer Science and Business Media Deutschland GmbH, 2023. p. 215-223 (Lecture Notes in Networks and Systems; Vol. 491).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus

AU - Shetty, Jayashree S.

AU - Shetty, Nisha P.

AU - Das, Vedant Rishi

AU - Vaibhav,

AU - Olivia, Diana

PY - 2023

Y1 - 2023

N2 - Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80% was recorded on the ensemble method.

AB - Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80% was recorded on the ensemble method.

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M3 - Conference contribution

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BT - Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022

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Shetty JS , Shetty NP, Das VR, Vaibhav , Olivia D. Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus. In Dutta P, Chakrabarti S, Bhattacharya A, Dutta S, Piuri V, editors, Emerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 215-223. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-4193-1_20

Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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